基于AIS信息校准的双频地波雷达的船只融合跟踪

高频地波雷达（HFSWR）和自动船只确认系统（AIS）是船只跟踪的重要传感器。高频地波雷达可以用来跟踪探测区域的所有船只,而AIS只能用来确认合作船只的信息。由于海杂波的干扰,使用单频率地波雷达的船只跟踪会淹没在布拉格峰值的盲区里,改变探测频率是克服这一缺点的有效手段。在这种背景下,我们提出一种基于AIS校准的双频雷达融合探测算法。因为不同频率的地波雷达测量与AIS的测量值存在系统误差,所以AIS信息可以用来估计和校准地波雷达的每个频率的系统误差。首先,将合作目标的点迹测量与地波雷达的点迹测量通过JVC分配算法进行点迹关联。从合作船只的点迹关联结果中,双频雷达的系统误差可以估计和校准。其次,基于校准的双频雷达数据,使用融合JPDA-UKF算法进行船只跟踪。通过真实探测的数据的实验结果显示所提算法可以实时跟踪船只,相比单频率跟踪可以进一步提高跟踪能力和跟踪精度。     

Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

The fine-scale swimming behavior of fish can now be studied because of the development of sophisticated measurement devices such as multibeam sonar and stereo video systems. However, even with these sensors, improved methods are still required to generate quality estimates of swimming speeds and turn rates. Biologists have commonly relied on pointwise differentiation of noisy position measurements while engineers have focused on Bayesian algorithms to track underwater vehicles. A comparative evaluation of the performance of these tracking algorithms for the analysis of fine-scale behavior of fish was performed using a data set of 100 fish tracks recorded simultaneously with a multibeam sonar and a stereo video camera system. The segmenting track identifier, a non-Bayesian curve fitting and segmenting tracker, is shown to be most effective for tracking the unpredictable and complex horizontal motion of fish while a Kalman smoother using a constant-velocity model is shown to be most effective for tracking the more predictable and piecewise linear vertical motion of fish. Both are shown to be more effective than pointwise differentiation. Criteria for selecting an appropriate algorithm for a given motion study are provided     

基于CT非线性模型的水下目标跟踪算法比较

针对水下目标跟踪非线性跟踪精度问题,假设目标机动模型为恒转速运动模型,贝叶斯框架下,因扩展卡尔曼滤波跟踪方法进行模型在估计点的泰勒展开,忽略一阶以上高阶项,存在模型误差,比较了扩展卡尔曼滤波、无迹卡尔曼滤波、容积卡尔曼滤波在高斯噪声干扰下滤波误差均方根,以及3种方法运行时间。仿真证明,非线性系统下状态维度为5,容积卡尔曼滤波跟踪的精度高于无迹卡尔曼滤波,无迹卡尔曼滤波高于扩展卡尔曼滤波。该研究为海上目标非线性测量系统提供仿真实例,为进一步滤波算法改进提供基础。     

The use of artificial neural networks for the intelligent optimalcontrol of surface ships

Many conventional ship autopilots use proportional integral and derivative (PID) control algorithms to guide a ship on a fixed heading (course-keeping) or a new heading (course-changing). Such systems usually have a gyrocompass as a single sensory input. Modern sea going vessels have a range of navigation aids most of which may be interconnected to form integrated systems. It is possible to employ the navigational data to provide best estimates of state vectors (Kalman filter) and optimal guidance strategies. Such techniques require powerful computing facilities, particularly if the dynamic characteristics of the vessel are changing, as may be the case in a maneuvering situation or changes in forward speed. This paper investigates the possibility of training a neural network to behave in the same manner as an optimal ship guidance system, the objective being to provide a system that can adapt its parameters so that it provides optimal performance over a range of conditions, without incurring a large computational penalty. A series of simulation studies have been undertaken to compare the performance of a trained neural network with that of the original optimal guidance system over a range of forward speeds. It is demonstrated that a single network has comparable performance to a set of optimal guidance control laws, each computed for different forward speeds     

Portable acoustic tracking system for divers (PATS)

A portable acoustic tracking system (PATS) has been developed for the Navy to track underwater construction teams in water depths to 200 ft. The tracking system can be deployed and operated by the divers and is intended for use in remote areas and for nearshore survey, maintenance, and construction work. A self-calibrating system provides a geographically justified track for up to eight divers. Diver position coordinates are computed from acoustic measurements at periodic intervals and displayed for control purposes. The positions are also recorded for future reference.     

传感器测量衰减下的水下目标纯方位跟踪算法

为解决多传感器水下目标纯方位跟踪中的传感器测量衰减问题,建立水下目标静态多传感器纯方位跟踪模型,将传感器测量衰减建模为统计特性已知的随机变量,基于融合中心接收到的各水声传感器的原始测量值,设计了一种集中式状态估计器结构,利用最小方差方法推导出最优的集中式目标状态估计增益。通过算例仿真可以得出,所提出的算法能够在水声传感器不做机动的前提下跟踪目标,弥补了单个水声传感器观测性不足的缺点,对比传统的集中式Kalman估计器,具有更高的精度,能够有效解决传感器测量衰减问题。     

Robust leader-follower formation tracking control of multiple underactuated surface vessels

This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation.The formation is achieved by the follower to track a virtual target defined relative to the leader.A robust adaptive target tracking law is proposed by using neural network and backstepping techniques.The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces,nonlinear damping,unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning.Based on Lyapunov analysis,the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin.Simulation results demonstrate the effectiveness of the control strategy.     

An assessment of fuzzy logic vessel path control

A fuzzy logic controller for ship path control in restricted waters is developed and evaluated. The controller uses inputs of heading, yaw rate, and lateral offset from the nominal track to produce a commanded rudder angle. Input variable fuzzification, fuzzy associative memory rules, and output set defuzzification are described. Two maneuvering situations are evaluated: track keeping along a specified path where linearized regulator control is valid; and larger maneuvers onto a specified path where nonlinear modeling and control are required. For the track keeping assessment, the controller is benchmarked against a conventional linear quadratic Gaussian (LQG) optimal controller and Kalman filter control system. The Kalman filter is used to produce the input state variable estimates for the fuzzy controller as well. An initial startup transient and regulator control performance with an external hydrodynamic disturbance are evaluated using linear model simulations of a crude oil tanker. A fully nonlinear maneuvering model for a smaller product tanker is used to assess the larger maneuvers     

南海中尺度涡的演化过程与行为追踪:一种基于全局最邻近滤波的方法

The eddy tracking approach is developed using the global nearest neighbor filter(GNNF) to investigate the evolution processes and behaviors of mesoscale eddies in the South China Sea(SCS). Combining the Kalman filter and optimal data association technologies, the GNNF algorithm is able to reduce pairing errors to 0.2% in tracking synthetic eddy tracks, outperforming other existing methods. A total of 4 913 eddy tracks that last more than a week are obtained by the GNNF during 1993–2012. The analysis of a growth and a decay based on 3 445 simple eddy tracks show that eddy radius, amplitude, and vorticity smoothly increase during the first half of lifetime and decline during the second half following a parabola opening downwards. The genesis of eddies mainly clusters northwest and southwest of Luzon Island whereas the dissipations concentrate the Xisha Islands where the underwater bay traps and terminates eddies. West of the Luzon Strait, northwest of Luzon Island, and southeast of Vietnam are regions where eddy splits and mergers are frequently observed. Short disappearances mainly distribute in the first two regions. Moreover, eddy splits generally result in a decrease of the radius and the amplitude whereas eddy mergers induce growing up. Eddy intensity and vorticity, on the contrary, are strengthened in the eddy splits and diminished in mergers.     

Inversion for Time-Evolving Sound-Speed Field in a Shallow Ocean by Ensemble Kalman Filtering

In the context of the recent Maritime Rapid Environmental Assessment/Blue Planet 2007 sea trial (MREA/BP07), this paper presents a range-resolving tomography method based on ensemble Kalman filtering of full-field acoustic measurements, dedicated to the monitoring of environmental parameters in coastal waters. The inverse problem is formulated in a state–space form wherein the time-varying sound-speed field (SSF) is assumed to follow a random walk with known statistics and the acoustic measurements are a nonlinear function of the SSF and the bottom properties. The state–space form enables a straightforward implementation of a nonlinear Kalman filter, leading to a data assimilation problem. Surface measurements augment the measurement vector to constrain the range-dependent structure of the SSF. Realistic scenarios of vertical slice shallow-water tomography experiments are simulated with an oceanic model, based on the MREA/BP07 experiment. Prior geoacoustic inversion on the same location gives the bottom acoustic properties that are input to the propagation model. Simulation results show that the proposed scheme enables the continuous tracking of the range-dependent SSF parameters and their associated uncertainties assimilating new measurements each hour. It is shown that ensemble methods are required to properly manage the nonlinearity of the model. The problem of the sensitivity to the vertical array (VA) configuration is also addressed.      

Sonar tracking of multiple targets using joint probabilistic data association

The problem of associating data with targets in a cluttered multi-target environment is discussed and applied to passive sonar tracking. The probabilistic data association (PDA) method, which is based on computing the posterior probability of each candidate measurement found in a validation gate, assumes that only one real target is present and all other measurements are Poisson-distributed clutter. In this paper, a new theoretical result is presented: the joint probabilistic data association (JPDA) algorithm, in which joint posterior association probabilities are computed for multiple targets (or multiple discrete interfering sources) in Poisson clutter. The algorithm is applied to a passive sonar tracking problem with multiple sensors and targets, in which a target is not fully observable from a single sensor. Targets are modeled with four geographic states, two or more acoustic states, and realistic (i.e., low) probabilities of detection at each sample time. A simulation result is presented for two heavily interfering targets illustrating the dramatic tracking improvements obtained by estimating the targets' states using joint association probabilities.     

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.     

Target localization and tracking from Doppler-shift measurements

It is possible to localize a nonmaneuvering moving source radiating a constant frequency tone from measurements of the Doppler-shifted frequency at several sensors. Due to the nonlinear nature of the problem, it is necessary to find the solution by grid searches. However, if measurements of the rates of frequency changes are available, the search is only in three dimensions instead of the normal five in source frequency, its x-y positions, and speeds. The validity of combining frequency and frequency-rate measurements is confirmed with simulation studies. The overall system includes a least-squares track-sort algorithm to differentiate the true track from the extraneous track, and a Kalman tracker for the prediction of future source positions, thereby reducing the grid search size. An error analysis relating localization accuracy to uncertainties in frequency measurements and sensor positions is also given     

Vision-based system of AUV for an underwater pipeline tracker

This paper describes a new framework for detection and tracking of underwater pipeline,which includes software system and hardware system.It is designed for vision system of AUV based on monocular CCD camera.First,the real-time data flow from image capture card is pre-processed and pipeline features are extracted for navigation.The region saturation degree is advanced to remove false edge point group after Sobel operation.An appropriate way is proposed to clear the disturbance around the peak point in the process of Hough transform.Second,the continuity of pipeline layout is taken into account to improve the efficiency of line extraction.Once the line information has been obtained,the reference zone is predicted by Kalman filter.It denotes the possible appearance position of the pipeline in the image.Kalman filter is used to estimate this position in next frame so that the information of pipeline of each frame can be known in advance.Results obtained on real optic vision data in tank experiment are displayed and discussed.They show that the proposed system can detect and track the underwater pipeline online,and is effective and feasible.     

Joint direction-of-arrival and array-shape tracking for multiplemoving targets

The problem of tracking the directions-of-arrival (DOAs) of multiple moving sonar targets with an array of passive sensors is complicated by sensor movement. An algorithm for the joint tracking of source DOAs and sensor positions is presented to address this problem. Initial maximum-likelihood estimates of source DOAs and sensor positions are refined by Kalman filtering. Spatio-temporally correlated array movement is considered. Source angle dynamics are used to achieve correct data association. The new technique is capable of performing well for the difficult cases of sources that cross in angle as well as for fully coherent sources. Computer simulations show that the approach is robust in the presence of array motion modeling uncertainty and effectively reduces dependence on expensive and possibly unreliable hardware     

NNFFC-adaptive output feedback trajectory tracking control for a surface ship at high speed

An adaptive output feedback controller based on neural network feedback-feedforward compensator (NNFFC) which drives a surface ship at high speed to track a desired trajectory is designed. The tracking problem of the surface ship at low speed has been widely investigated. However, the coupling interactions among the forces from each degree of freedom (DOF) have not been considered in general. Furthermore, the influence of the hydrodynamic damping is also simplified into a linear form or neglected. On the contrary, coupling interactions and the nonlinear characteristics of the hydrodynamic damping can never be neglected in high speed maneuvering situation. For these reasons, the influence of the nonlinear hydrodynamic damping on the tracking precision is considered in this paper. Since the hydrodynamic coefficients of the surface ship at high speed are very difficult to be accurately estimated as a prior, it will be compensated by NNFFC as an unknown part of the tracking dynamics system. The stability analysis will be given by the Lyapunov theory. It indicates that the proposed control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB), and numerical simulations can illustrate the excellent tracking performance of the surface ship at high speed under the proposed control scheme.     

Determining the total ozone from geostationary earth satellites

A method for determining the total ozone (TO) with high spatial (3×3 km²) and temporal (15 min) resolutions by using measurements of the Earth’s outgoing thermal radiation from Meteosat geostationary satellites is proposed. The method is based on measurements with a SEVIRI instrument (eight IR channels) and involves additional information on the three-dimensional field of the atmospheric temperature and on the surface temperature obtained from polar satellites (AIRS instrument). The inverse problem of TO determination is solved by the method of neural networks. TO measurements with the AIRS instrument are also used for training the neural networks. Ground-based TO measurements at the international ozonometric network are used for controlling the quality of AIRS data and detecting the errors of the proposed method of TO determination from SEVIRI data. The mean and rms differences between TO values obtained with the use of the proposed method and from the results of measurements at the international ozonometric network are shown to be 1.5 and 6.5%, respectively. Examples of TO distributions reconstructed with high spatial and temporal resolutions are presented. These examples show that the elaborated method for solving various scientific and applied problems and, in particular, for investigating stratospheric dynamics is promising.     

GMS云图结合天气图在奇异路径台风研究中的应用——8616号台风的分析

奇异路径的台风是最为复杂、最难以预报的天气形势,特别是8616号台风,其路径更为奇特,在海上回旋、停滞、打转、突然转向,持续时间长达23天之久,是历史上罕见的。本文利用GMS云图和天气图对其进行跟踪分析,在此基础上,着重对造成其路径复杂的主要原因进行分析归纳,得出了有一定参考价值的结论。     

Derivation of coastal wind and wave parameters from offshore measurements of TOPEX satellite using ANN

The paper discusses an artificial neural network (ANN) approach to project information on wind speed and waves collected by the TOPEX satellite at deeper locations to a specified coastal site. The observations of significant wave heights, average wave period and wind speed at a number of locations over a satellite track parallel to a coastline are used to estimate corresponding values of these three parameters at the coastal site of interest. A combined network involving an input and output of all the three parameters, viz., wave height, period and wind speed instead of separate networks for each one of these variables was found to be necessary in order to train the network with sufficient flexibility. It was also found that network training based on statistical homogeneity of data sets is essential to obtain accurate results. The problem of modeling wind speeds that are always associated with very high variations in their magnitudes was tackled in this study by imparting training in an innovated manner.     

Compensation for source heave by use of a Kalman filter

This paper presents a procedure for data filtering to compensate for the effects of the towed body dynamics (heave), in shallow marine seismic reflection records. A method to extract an approximate record of the heave contribution to data collected is outlined. The method utilizes the time to water-sediment interface on each acoustic return record to construct the required approximate heave motion record. The frequency response of the heave component record provides the basis for a proposed linear model for the heave motion. A formulation of the heave compensation requirement as a Kalman filtering problem in optimal linear estimation theory is given. A discussion of the computational aspects and practical results are discussed to conclude the paper.